Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies.
To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media.
This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018.
The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracperceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.Erectile dysfunction, especially in younger men, is an early sign of cardiovascular disease and may decrease quality of life. Men may be motivated to adopt a healthy dietary pattern if it lowers their risk of erectile dysfunction.
To assess the association between adherence to a diet quality index based on healthy dietary patterns and erectile dysfunction in men.
This population-based prospective cohort study included men from the Health Professionals Follow-up Study with follow-up from January 1, 1998, through January 1, 2014. Participants included US male health professionals aged 40 to 75 years at enrollment. https://www.selleckchem.com/products/itd-1.html Men with erectile dysfunction or a diagnosis of myocardial infarction, diabetes, stroke, or genitourinary cancer at baseline were excluded. Analyses were completed in February 2020.
A food frequency questionnaire was used to determine nutrient and food intake every 4 years.
Diet quality was assessed by Mediterranean Diet score and the Alternative Healthy Eating Index 2010 score, with higher er Mediterranean diet scores were also inversely associated with incident erectile dysfunction among older men (age 60 to &lt;70 years HR, 0.82; 95% CI, 0.76-0.89; age ?70 years HR, 0.93; 95% CI, 0.86-1.00). Men scoring in the highest quintile of the Alternative Healthy Eating Index 2010 also had a lower risk of incident erectile dysfunction, particularly among men age younger than 60 years (quintile 5 vs quintile 1 HR, 0.78; 95% CI, 0.63-0.97).
This cohort study found that adherence to healthy dietary patterns was associated with a lower risk for erectile dysfunction, suggesting that a healthy dietary pattern may play a role in maintaining erectile health.
This cohort study found that adherence to healthy dietary patterns was associated with a lower risk for erectile dysfunction, suggesting that a healthy dietary pattern may play a role in maintaining erectile health.This study offers a rare opportunity to evaluate life-course differences in the likelihood of developing major depressive disorder (MDD) after exposure to georeferenced neighborhood-level violence during an armed conflict.
To examine age cohort (age &lt;11 vs ?11 years) differences in associations of neighborhood-level violence with subsequent depression onset, independently of individual exposure and other key characteristics.
The Chitwan Valley Family Study is a population-representative panel study (1995 to present) conducted in Western Chitwan in Nepal, a low-income country that experienced a medium-intensity armed conflict from 2000 to 2006. Data for violent events were collected during the armed conflict and were linked to lifetime histories of MDD (collected in 2016-2018). The present cohort study analyzes 10?623 participants within 151 neighborhoods, systematically selected and representative of Western Chitwan. All residents aged 15 to 59 years at MDD assessment were eligible (response rate,?93entions. Future research should consider other disorders, other types of violence, and elderly individuals.Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning.
In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N?=?5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness.
Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries all models achieved better performances on the China cohorts.
When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies.
Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.
Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.